Source code for sym.ops.polynomial_camera_cal.lie_group_ops

# -----------------------------------------------------------------------------
# This file was autogenerated by symforce from template:
#     ops/CLASS/lie_group_ops.py.jinja
# Do NOT modify by hand.
# -----------------------------------------------------------------------------

# ruff: noqa: PLR0915, F401, PLW0211, PLR0914

from __future__ import annotations

import math
import typing as T

import numpy

import sym


class LieGroupOps(object):
    """
    Python LieGroupOps implementation for :py:class:`symforce.cam.polynomial_camera_cal.PolynomialCameraCal`.
    """

    @staticmethod
    def from_tangent(vec: numpy.ndarray, epsilon: float) -> sym.PolynomialCameraCal:
        # Total ops: 0

        # Input arrays
        if vec.shape == (8,):
            vec = vec.reshape((8, 1))
        elif vec.shape != (8, 1):
            raise IndexError(
                "vec is expected to have shape (8, 1) or (8,); instead had shape {}".format(
                    vec.shape
                )
            )

        # Intermediate terms (0)

        # Output terms
        _res = [0.0] * 8
        _res[0] = vec[0, 0]
        _res[1] = vec[1, 0]
        _res[2] = vec[2, 0]
        _res[3] = vec[3, 0]
        _res[4] = vec[4, 0]
        _res[5] = vec[5, 0]
        _res[6] = vec[6, 0]
        _res[7] = vec[7, 0]
        return sym.PolynomialCameraCal.from_storage(_res)

    @staticmethod
    def to_tangent(a: sym.PolynomialCameraCal, epsilon: float) -> numpy.ndarray:
        # Total ops: 0

        # Input arrays
        _a = a.data

        # Intermediate terms (0)

        # Output terms
        _res = numpy.zeros(8)
        _res[0] = _a[0]
        _res[1] = _a[1]
        _res[2] = _a[2]
        _res[3] = _a[3]
        _res[4] = _a[4]
        _res[5] = _a[5]
        _res[6] = _a[6]
        _res[7] = _a[7]
        return _res

    @staticmethod
    def retract(
        a: sym.PolynomialCameraCal, vec: numpy.ndarray, epsilon: float
    ) -> sym.PolynomialCameraCal:
        # Total ops: 8

        # Input arrays
        _a = a.data
        if vec.shape == (8,):
            vec = vec.reshape((8, 1))
        elif vec.shape != (8, 1):
            raise IndexError(
                "vec is expected to have shape (8, 1) or (8,); instead had shape {}".format(
                    vec.shape
                )
            )

        # Intermediate terms (0)

        # Output terms
        _res = [0.0] * 8
        _res[0] = _a[0] + vec[0, 0]
        _res[1] = _a[1] + vec[1, 0]
        _res[2] = _a[2] + vec[2, 0]
        _res[3] = _a[3] + vec[3, 0]
        _res[4] = _a[4] + vec[4, 0]
        _res[5] = _a[5] + vec[5, 0]
        _res[6] = _a[6] + vec[6, 0]
        _res[7] = _a[7] + vec[7, 0]
        return sym.PolynomialCameraCal.from_storage(_res)

    @staticmethod
    def local_coordinates(
        a: sym.PolynomialCameraCal, b: sym.PolynomialCameraCal, epsilon: float
    ) -> numpy.ndarray:
        # Total ops: 8

        # Input arrays
        _a = a.data
        _b = b.data

        # Intermediate terms (0)

        # Output terms
        _res = numpy.zeros(8)
        _res[0] = -_a[0] + _b[0]
        _res[1] = -_a[1] + _b[1]
        _res[2] = -_a[2] + _b[2]
        _res[3] = -_a[3] + _b[3]
        _res[4] = -_a[4] + _b[4]
        _res[5] = -_a[5] + _b[5]
        _res[6] = -_a[6] + _b[6]
        _res[7] = -_a[7] + _b[7]
        return _res

    @staticmethod
    def interpolate(
        a: sym.PolynomialCameraCal, b: sym.PolynomialCameraCal, alpha: float, epsilon: float
    ) -> sym.PolynomialCameraCal:
        # Total ops: 24

        # Input arrays
        _a = a.data
        _b = b.data

        # Intermediate terms (0)

        # Output terms
        _res = [0.0] * 8
        _res[0] = _a[0] + alpha * (-_a[0] + _b[0])
        _res[1] = _a[1] + alpha * (-_a[1] + _b[1])
        _res[2] = _a[2] + alpha * (-_a[2] + _b[2])
        _res[3] = _a[3] + alpha * (-_a[3] + _b[3])
        _res[4] = _a[4] + alpha * (-_a[4] + _b[4])
        _res[5] = _a[5] + alpha * (-_a[5] + _b[5])
        _res[6] = _a[6] + alpha * (-_a[6] + _b[6])
        _res[7] = _a[7] + alpha * (-_a[7] + _b[7])
        return sym.PolynomialCameraCal.from_storage(_res)